
SAS Payment Integrity for Social Benefits detects and prevents fraud in social benefits programs using advanced analytics, AI, and machine learning. It integrates data from various sources to identify anomalies, ensuring payment integrity and reducing losses from fraud, waste, and abuse.
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SAS
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SAS Payment Integrity for Social Benefits
Detect and prevent fraud at every stage of the benefit claims process for unemployment insurance, food or cash assistance, housing assistance, child-care programs and more. Running on SAS Viya, our solution promotes payment integrity while fighting fraud and abuse with the fastest, most productive data and AI.
Detect more fraud, reduce your losses & optimize payment integrity
Spot more payment integrity breaches than ever before with a robust fraud analytics engine that processes all data (not just a sample) in real time or in batch. Running on the powerful SAS Viya platform, the solution uses advanced analytics with embedded artificial intelligence (AI) and machine learning algorithms, combined with other techniques – business rules, text mining, database searches, exception reporting, network link analysis, etc. – to uncover more suspicious activity faster with greater accuracy.
Gain a consolidated view of fraud risk & empower limited resources
Identify linkages among seemingly unrelated claims with a unique visualization interface that lets you go beyond individual and account views to analyze all related activities and relationships at a network dimension. Social network diagrams and sophisticated data mining capabilities give you a better understanding of new threats, enabling you to identify more schemes than ever before and prevent big losses early. Your agency can gain a comprehensive and holistic view that can help expedite program audits and investigations by automatically linking key data sources to quickly identify networks with suspicious activity.
Reduce false positives while boosting efficiency
SAS Payment Integrity for Social Benefits applies risk- and value-based scoring models to accurately score and prioritize alerts before they go to analysts and investigators. With the time saved, valuable personnel can work more cases with greater efficiency and focus on higher-value networks that generate a better ROI. More accurate scoring also means fewer false positives. Multiple disciplines can work together in a unified way to support an analytics-driven approach that empowers governments to control costs and maintain social program integrity.
Key features
Our unified platform for program integrity equips staff with powerful tools for analyzing, detecting and preventing payment integrity issues across various public assistance programs.
Data management
Seamlessly integrate any enterprise data source across systems, regardless of format (e.g., notes in claims files) and apply embedded data quality techniques to improve accuracy. This creates a holistic view of a recipient or provider for better detection of anomalies or discrepancies across government programs or systems.
Hybrid analytics approach
Our hybrid analytics approach combines anomaly detection, rules and predictive modeling to identify fraud, waste and abuse earlier than traditional methods.
Advanced analytics with embedded AI
Provides a broad set of advanced analytic and AI techniques, including modern statistical, machine learning, deep learning and text analytics algorithms.
Rule & analytic model management
Includes prepackaged heuristic rules, anomaly detection and predictive models. Lets you create and logically manage business rules, analytic models, alerts and watch lists.
Detection & prioritized alert generation
Calculates the propensity for fraud at first submission with a scoring engine that combines business rules, anomaly detection and advanced analytics; then rescores claims at each processing stage as new claims data is captured.
Social network analysis
Provides a unique visualization interface that lets you go beyond transaction and account views to analyze related activities and relationships at a network dimension.
Open source compatibility
Language-agnostic programming enables data scientists to access SAS algorithms via open source programming interfaces – including R, Python, Java and Lua – from applications such as Jupyter Notebook.
Flexibility & security
Offers the fast, flexible benefits of a secure, cloud-ready environment. Scales to every analytic level, and lets you choose the solution package that works best for your organization.